1. Field of the Invention (Technical Field)
The present invention relates to image classification and recognition based on spatial frequency sampling as well as image domain sampling.
2. Background Art
In the early 1970""s, the methods used in automatic pattern recognition could be grouped into two categories: those based on edge and edge-angle correlation; and those based on power-spectral density. In connection with aerial images and remote sensing, U.S. Pat. No. 3,687,772 disclosed a robotic-eye photodetector called a ring-wedge photodetector. As shown in FIG. 1, this photodetector preferably has 32 separate annular semi-rings for sampling power spectral density, independently of rotation, and 32 pie-shaped segments devised for readout of edges and edge-angle correlation. The wedge data are scale-invariant. In the recognition system disclosed, the photodetector is placed in the back focal plane of a Fourier-optical processor. The entire system includes a laser illuminator, an input picture being inspected, a Fourier-transform lens, and the ring-wedge photodetector in the back focal plane that is also known as the optical-transform plane. Each of the 64 photodetectors on the ring-wedge photodetector has a separate amplifier and digitizer so that the sampled Fourier transform signal can be coupled into an electronic digital computer.
By gathering data in rings and wedges, very complicated pictures with ten million or so pixels could be coarsely sampled in the optical Fourier transform space and automatic recognition tasks could be accomplished using only 64 feature values. During the following two decades, this analog hybrid opto-electronic system was successful in establishing high accuracy classification and recognition at high speeds (on the order of one decision per millisecond). In recognition of high-resolution photographs it is comparable in accuracy to the matched-filter (an earlier form of prior art), but it is much easier to implement. Notable successes were sorting of photographs of cats versus dogs, black-lung disease determinations, sharpness analysis of hypodermic syringes, wafer inspection, CD inspection, surface roughness determinations, and particle sizing. This system is particularly applicable when the decision/recognition depends upon fine-scale features or texture.
The present invention provides a software system both for recognition and classification of digital images automatically and rapidly. This system is particularly suited to all-digital optical robotics. It can be implemented as a stand-alone system or as a xe2x80x9ctool-kitxe2x80x9d in a general image processing environment that includes other means for processing, filing, and sorting images. The all-digital system of the invention, using both spatial transform features and image features, can automatically classify or recognize any image group with high accuracy, matching the best performance that human photointerpreters are capable of. The software recognition system is highly advantageous over the prior art in that it is affordable, costing only a small fraction of that for the laser-optical hybrid of the prior art. It is user friendly because neural network training routines yield superior decision performance. The invention has demonstrated superior performance in a wide variety of applications including classification of image quality in a manner that is widely independent of scene content, recognition of handwriting in a manner widely independent of textual content, and classification into multiple bins or categories. Go and no-go production testing quality of images used in photolithography and automatic sizing of particles as in talcum or pharmaceuticals are all viable application areas for this invention. The present invention is also applicable in a robotic control of the shutter of a smart camera, i.e., a picture is taken only if the recognition system okays the scene content, erg., for sharpness of image, that eyes are all open, for smiling expressions, and the like.
The present invention is of computer software for, computer media with computer software for, and a method of calculating ring-wedge data from a digital image comprising performing a discrete Fourier transform of the digital image. In the preferred embodiment, a calculation is also performed of discrete autocorrelation, discrete cosine transform, and/or Hadamard transform. The ring wedge sampling preferably comprises calculating             m      j        =                  ∑                  u          =          0                          N          -          1                    ⁢              xe2x80x83            ⁢                        ∑                      v            =            0                                M            -            1                          ⁢                  xe2x80x83                ⁢                              "LeftBracketingBar"                                          F                ~                            ⁢                              xe2x80x83                            ⁢                              (                                  u                  ,                  v                                )                                      "RightBracketingBar"                    ⁢                      xe2x80x83                    ⁢                                    M              ~                        j                    ⁢                      xe2x80x83                    ⁢                      (                          u              ,              v                        )                                ,
where mj is a jth measurement over a sampling area to which each pixel""s degree of membership is given by {tilde over (M)}j(u,v),                     F        ~            ⁢              xe2x80x83            ⁢              (                  u          ,          v                )              =                  ∑                  n          =          0                          N          -          1                    ⁢              xe2x80x83            ⁢                        ∑                      m            =            0                                M            -            1                          ⁢                  xe2x80x83                ⁢                  f          ⁢                      xe2x80x83                    ⁢                      (                          n              ,              m                        )                    ⁢                      xe2x80x83                    ⁢                      exp            ⁡                          [                                                -                  2                                ⁢                                  xe2x80x83                                ⁢                π                ⁢                                  xe2x80x83                                ⁢                                  (                                                            un                      N                                        +                                          vm                      N                                                        )                                            ]                                            ,
where f(n,m) comprises digital image pixel values with 0xe2x89xa6n less than N, 0xe2x89xa6u less than N, 0xe2x89xa6m less than M, and 0xe2x89xa6v less than M. The sampling calculation preferably determines each pixel""s degree of membership by employing sampling regions for ring and wedge regions defined as:                     R        j            =              {                                                            (                                                      f                    x                                    ,                                      f                    y                                                  )                            ⁢                              :                            ⁢                              xe2x80x83                            ⁢                              ρ                j                                      ≤                                                            f                  x                  2                                +                                  f                  y                  2                                                       less than                                           ρ                j                            +                              Δ                ⁢                                  xe2x80x83                                ⁢                                  ρ                  j                                                              ,                                    φ              min                        ≤                                          tan                                  -                  1                                            ⁢                              xe2x80x83                            ⁢                                                f                  y                                                  f                  x                                                       less than                                           φ                min                            +              π                                      }              ,          
        ⁢    and                      R        j            =              {                                                            (                                                      f                    x                                    ,                                      f                    y                                                  )                            ⁢                              :                            ⁢                              xe2x80x83                            ⁢                              ρ                min                                      ≤                                                            f                  x                  2                                +                                  f                  y                  2                                                       less than                           ρ              max                                ,                                    φ              j                        ≤                                          tan                                  -                  1                                            ⁢                              xe2x80x83                            ⁢                                                f                  y                                                  f                  x                                                       less than                                           φ                j                            +                              Δ                ⁢                                  xe2x80x83                                ⁢                                  φ                  j                                                                    }              ,  
where pj is the radial distance from the origin to the inner radius of the jth detector region, and xcex94xcfx81j is its radial width, and xcfx86j is the angular distance from the fx axis to the leading edge of the jth detector region and xcex94xcfx86j is its angular width. The sampling may be accomplished by determining each pixel""s degree of membership as appropriate for either of two preferred methods: bin-summing or mask-summing, as in the descriptions to follow. The ring-wedge data is preferably provided to a neural network (most preferably a fully connected, three-layer, feed-forward neural network with sigmoidal activation functions) to perform pattern recognition on the data. The neural network may be implemented in hardware or software, as well understood by one of ordinary skill in the art. The ring-wedge data may be used in analysis of an images such as fingerprint images, images of particles, images of human faces, and satellite images, and the analysis may be for tasks such as object recognition, image quality assessment, and image content classification.
A primary object of the invention is to allow for the rapid prototyping of practical recognition systems by providing a highly effective, consistent data format for making recognition decisions.
Another object of the invention is to provide the ability to apply either of two preferred methods to any number of subshades from a single input image. In this way spatial location and other image domain information can be combined with the ring-wedge format to produce superior recognition.
A primary advantage of the invention is the provision of a digitally calculated set of dimensionally reduced data to permit practical machine learning methods to determine appropriate means of separating these data into redefined groupings.
Another advantage of the invention is that its straightforward input-to-output data flow admits it to a modular design structure allowing easy interaction with other data processing methods.